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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Choose T4 Runtime to start GPU example,  \n",
        "\n",
        "Runtime can change by:\n",
        "1. Menu: Code Execution -> Change Runtime Type\n",
        "2. Choose: `Python 3` , Hardware Accelerator : `T4`\n"
      ],
      "metadata": {
        "id": "wsZ3_cLouMy0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuP53zvt4q3H",
        "outputId": "9a7432c3-3111-48a8-8bcb-81428f166a01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Jan 14 07:34:48 2025       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   49C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sTAJbqd4VkB",
        "outputId": "a7b2c864-1176-41e8-ccc9-a0aa8df285fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.10.12\n"
          ]
        }
      ],
      "source": [
        "!python --version\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install https://github.com/modelscope/dash-infer/releases/download/v2.0.0-rc3/dashinfer-2.0.0rc3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n",
        "\n",
        "# check dashinfer release url in : https://github.com/modelscope/dash-infer/releases/tag/v2.0.0-rc3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "ymaZqCDZ4vE-",
        "outputId": "9ba3854b-7cdb-44ad-c019-a96f10403686"
      },
      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "Collecting dashinfer==2.0.0rc3\n",
            "  Downloading https://github.com/modelscope/dash-infer/releases/download/v2.0.0-rc3/dashinfer-2.0.0rc3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (687.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m687.2/687.2 MB\u001b[0m \u001b[31m834.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers>=4.40.0 in /usr/local/lib/python3.10/dist-packages (from dashinfer==2.0.0rc3) (4.47.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from dashinfer==2.0.0rc3) (2.5.1+cu121)\n",
            "Collecting ruamel.yaml (from dashinfer==2.0.0rc3)\n",
            "  Downloading ruamel.yaml-0.18.10-py3-none-any.whl.metadata (23 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (0.27.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (0.21.0)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (0.5.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.40.0->dashinfer==2.0.0rc3) (4.67.1)\n",
            "Collecting ruamel.yaml.clib>=0.2.7 (from ruamel.yaml->dashinfer==2.0.0rc3)\n",
            "  Downloading ruamel.yaml.clib-0.2.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.7 kB)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->dashinfer==2.0.0rc3) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->dashinfer==2.0.0rc3) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->dashinfer==2.0.0rc3) (3.1.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->dashinfer==2.0.0rc3) (2024.10.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch->dashinfer==2.0.0rc3) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch->dashinfer==2.0.0rc3) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->dashinfer==2.0.0rc3) (3.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.40.0->dashinfer==2.0.0rc3) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.40.0->dashinfer==2.0.0rc3) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.40.0->dashinfer==2.0.0rc3) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.40.0->dashinfer==2.0.0rc3) (2024.12.14)\n",
            "Downloading ruamel.yaml-0.18.10-py3-none-any.whl (117 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.7/117.7 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ruamel.yaml.clib-0.2.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (722 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m722.2/722.2 kB\u001b[0m \u001b[31m49.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ruamel.yaml.clib, ruamel.yaml, dashinfer\n",
            "Successfully installed dashinfer-2.0.0rc3 ruamel.yaml-0.18.10 ruamel.yaml.clib-0.2.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install modelscope"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgZgwSfGiYMS",
        "outputId": "b7ccada0-2a37-4c06-c373-7ee72b77547e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting modelscope\n",
            "  Downloading modelscope-1.22.0-py3-none-any.whl.metadata (40 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/40.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.3/40.3 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.25 in /usr/local/lib/python3.10/dist-packages (from modelscope) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from modelscope) (4.67.1)\n",
            "Requirement already satisfied: urllib3>=1.26 in /usr/local/lib/python3.10/dist-packages (from modelscope) (2.3.0)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25->modelscope) (3.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25->modelscope) (2024.12.14)\n",
            "Downloading modelscope-1.22.0-py3-none-any.whl (5.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m91.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: modelscope\n",
            "Successfully installed modelscope-1.22.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "from dashinfer import allspark\n",
        "from dashinfer.allspark import *\n",
        "from dashinfer.allspark.engine import *\n",
        "from dashinfer.allspark.prompt_utils import PromptTemplate\n",
        "\n",
        "# Configuration\n",
        "in_memory = False\n",
        "device_list = [0] # single card by default, 4 cards replace with [0,1,2,3]\n",
        "model_name = \"qwen/Qwen2.5-1.5B-Instruct\"\n",
        "output_base_folder = \"model_output\"\n",
        "user_data_type = \"float16\" # most device supports float16\n",
        "use_modelscope = False\n",
        "\n",
        "# Download and prepare the model\n",
        "if use_modelscope:\n",
        "  import modelscope\n",
        "  from modelscope.utils.constant import DEFAULT_MODEL_REVISION\n",
        "  model_local_path = modelscope.snapshot_download(model_name, DEFAULT_MODEL_REVISION)\n",
        "else:\n",
        "  model_local_path = model_name\n",
        "\n",
        "safe_model_name = model_name.replace(\"/\", \"_\")\n",
        "model_convert_folder = os.path.join(output_base_folder, safe_model_name)\n",
        "\n",
        "# Initialize model and engine\n",
        "model_loader = allspark.HuggingFaceModel(model_local_path, safe_model_name,\n",
        "                                         in_memory_serialize=in_memory,\n",
        "                                         user_set_data_type=user_data_type)\n",
        "engine = allspark.Engine()\n",
        "\n",
        "# Load and serialize the model\n",
        "model_loader.load_model().serialize(engine, model_output_dir=output_base_folder).free_model()\n",
        "\n",
        "# Configure runtime settings\n",
        "runtime_cfg = model_loader.create_reference_runtime_config_builder(\n",
        "    safe_model_name, TargetDevice.CUDA, device_list, max_batch=8).max_length(2048).build()\n",
        "engine.install_model(runtime_cfg)\n",
        "engine.start_model(safe_model_name)\n",
        "\n",
        "if in_memory: model_loader.free_memory_serialize_file()\n",
        "\n",
        "# Prepare input\n",
        "input_str = \"How to protect our planet and build a green future?\"\n",
        "messages = [\n",
        "   {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
        "   {\"role\": \"user\", \"content\": PromptTemplate.apply_chatml_template(input_str)}\n",
        "]\n",
        "templated_input_str = model_loader.init_tokenizer().get_tokenizer().apply_chat_template(\n",
        "    messages, tokenize=False, add_generation_prompt=True)\n",
        "\n",
        "# Configure generation settings\n",
        "gen_cfg = model_loader.create_reference_generation_config_builder(runtime_cfg)\n",
        "gen_cfg.update({\"top_k\": 1})\n",
        "\n",
        "# Generate response\n",
        "status, handle, queue = engine.start_request_text(\n",
        "    safe_model_name, model_loader, templated_input_str, gen_cfg)\n",
        "generated_ids = []\n",
        "\n",
        "while True:\n",
        "   elements = queue.Get()\n",
        "   if elements:\n",
        "      generated_ids += elements.ids_from_generate\n",
        "   status = queue.GenerateStatus()\n",
        "   if status in [GenerateRequestStatus.GenerateFinished,\n",
        "                 GenerateRequestStatus.GenerateInterrupted]:\n",
        "      break\n",
        "\n",
        "# Decode and print output\n",
        "output_text = model_loader.init_tokenizer().get_tokenizer().decode(generated_ids)\n",
        "print(f\"Model: {model_name}\\nInput: {input_str}\\nOutput: {output_text}\")\n",
        "\n",
        "# Clean up\n",
        "engine.release_request(safe_model_name, handle)\n",
        "engine.stop_model(safe_model_name)\n",
        "print(f\"Model: {model_name} have been released.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "zSSH5e-A42yb",
        "outputId": "aaeb4787-a70a-4711-e8eb-ceed4b1ed022"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AllSpark python package start init.\n",
            "[Info] No Multi-NUMA support on CUDA Version.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/660 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3cbb12a3540449efb707be58b8a547f9"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/3.09G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/242 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20266f0d24ea4eb7b31876fe3a6d1fba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model config:\n",
            "{'vocab_size': 151936, 'max_position_embeddings': 32768, 'hidden_size': 1536, 'intermediate_size': 8960, 'num_hidden_layers': 28, 'num_attention_heads': 12, 'use_sliding_window': False, 'sliding_window': None, 'max_window_layers': 21, 'num_key_value_heads': 2, 'hidden_act': 'silu', 'initializer_range': 0.02, 'rms_norm_eps': 1e-06, 'use_cache': True, 'rope_theta': 1000000.0, 'rope_scaling': None, 'attention_dropout': 0.0, 'return_dict': True, 'output_hidden_states': False, 'output_attentions': False, 'torchscript': False, 'torch_dtype': torch.bfloat16, 'use_bfloat16': False, 'tf_legacy_loss': False, 'pruned_heads': {}, 'tie_word_embeddings': True, 'chunk_size_feed_forward': 0, 'is_encoder_decoder': False, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 'tie_encoder_decoder': False, 'max_length': 20, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'output_scores': False, 'return_dict_in_generate': False, 'forced_bos_token_id': None, 'forced_eos_token_id': None, 'remove_invalid_values': False, 'exponential_decay_length_penalty': None, 'suppress_tokens': None, 'begin_suppress_tokens': None, 'architectures': ['Qwen2ForCausalLM'], 'finetuning_task': None, 'id2label': {0: 'LABEL_0', 1: 'LABEL_1'}, 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, 'tokenizer_class': None, 'prefix': None, 'bos_token_id': 151643, 'pad_token_id': None, 'eos_token_id': 151645, 'sep_token_id': None, 'decoder_start_token_id': None, 'task_specific_params': None, 'problem_type': None, '_name_or_path': 'qwen/Qwen2.5-1.5B-Instruct', '_commit_hash': '989aa7980e4cf806f80c7fef2b1adb7bc71aa306', '_attn_implementation_internal': None, '_attn_implementation_autoset': False, 'transformers_version': '4.43.1', 'model_type': 'Qwen_v20', 'rotary_emb_base': 1000000.0, 'size_per_head': 128}\n",
            "model config:\n",
            "{'vocab_size': 151936, 'max_position_embeddings': 32768, 'hidden_size': 1536, 'intermediate_size': 8960, 'num_hidden_layers': 28, 'num_attention_heads': 12, 'use_sliding_window': False, 'sliding_window': None, 'max_window_layers': 21, 'num_key_value_heads': 2, 'hidden_act': 'silu', 'initializer_range': 0.02, 'rms_norm_eps': 1e-06, 'use_cache': True, 'rope_theta': 1000000.0, 'rope_scaling': None, 'attention_dropout': 0.0, 'return_dict': True, 'output_hidden_states': False, 'output_attentions': False, 'torchscript': False, 'torch_dtype': torch.bfloat16, 'use_bfloat16': False, 'tf_legacy_loss': False, 'pruned_heads': {}, 'tie_word_embeddings': True, 'chunk_size_feed_forward': 0, 'is_encoder_decoder': False, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 'tie_encoder_decoder': False, 'max_length': 20, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'output_scores': False, 'return_dict_in_generate': False, 'forced_bos_token_id': None, 'forced_eos_token_id': None, 'remove_invalid_values': False, 'exponential_decay_length_penalty': None, 'suppress_tokens': None, 'begin_suppress_tokens': None, 'architectures': ['Qwen2ForCausalLM'], 'finetuning_task': None, 'id2label': {0: 'LABEL_0', 1: 'LABEL_1'}, 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, 'tokenizer_class': None, 'prefix': None, 'bos_token_id': 151643, 'pad_token_id': None, 'eos_token_id': 151645, 'sep_token_id': None, 'decoder_start_token_id': None, 'task_specific_params': None, 'problem_type': None, '_name_or_path': 'qwen/Qwen2.5-1.5B-Instruct', '_commit_hash': '989aa7980e4cf806f80c7fef2b1adb7bc71aa306', '_attn_implementation_internal': None, '_attn_implementation_autoset': False, 'transformers_version': '4.43.1', 'model_type': 'Qwen_v20', 'rotary_emb_base': 1000000.0, 'size_per_head': 128}\n",
            "model data type: float16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/7.30k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "298aa6076ae741179fb08533c55d3ecb"
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        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "273fa565449f4457b3f72c71f967172f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Warning]Model loader: failed to parse JSON file: 'qwen/Qwen2.5-1.5B-Instruct/tokenizer.json' to get tokenizer type, using default \n",
            "                type(BPE), output may be abnormal if using JSON Mode\n",
            "rotary base:  1000000.0\n",
            "serialize_model_from_torch: quant config:None\n",
            "save asgraph to  model_output/qwen_Qwen2.5-1.5B-Instruct.asgraph\n",
            "save asparam to  model_output/qwen_Qwen2.5-1.5B-Instruct.asparam\n",
            "parse weight time:  143.47967290878296\n",
            "current allspark version full: 2.0.0/(GitSha1:163850f7) major[ 2 ] minor[ 0 ] patch[ 0 ] commit =  163850f7\n",
            "calculate md5 of asgraph =  e9b1457b6bf53984b8aa639ae3812f79\n",
            "torch build meta: \t only_convert_lora \t:  False\n",
            "build_model_from_torch: save model = true, time :  143.89367246627808\n",
            "Model qwen_Qwen2.5-1.5B-Instruct serialize finished, consume 143.96698021888733 seconds...\n",
            "stop word ids: [[151645], [151643]]\n",
            "Start Request with Generate Config:\n",
            "{'top_k': 1, 'top_p': 0.8, 'do_sample': True, 'early_stopping': True, 'repetition_penalty': 1.1, 'presence_penalty': 0.0, 'length_penalty': 1.0, 'temperature': 0.7, 'min_length': 0, 'no_repeat_ngram_size': 0, 'eos_token_id': 151645, 'seed': 779900457, 'stop_words_ids': [[151645], [151643]], 'max_length': 2048, 'vocab_type': <VocabType.VOCAB_TYPE_BPE: 3>}\n",
            "Model: qwen/Qwen2.5-1.5B-Instruct\n",
            "Input: How to protect our planet and build a green future?\n",
            "Output: Protecting the planet and building a green future is an important task that requires collective effort from individuals, communities, businesses, and governments around the world. Here are some ways we can work towards protecting our planet and creating a sustainable future:\n",
            "\n",
            "1. Reduce waste: We should reduce, reuse, and recycle as much as possible. This includes reducing consumption of single-use plastics, using reusable bags, bottles, and containers, and composting food scraps.\n",
            "\n",
            "2. Use renewable energy sources: Renewable energy sources such as solar, wind, hydroelectric, and geothermal power can help reduce greenhouse gas emissions and decrease reliance on fossil fuels.\n",
            "\n",
            "3. Conserve water: Water conservation measures such as fixing leaks, taking shorter showers, and using low-flow fixtures can help conserve this precious resource.\n",
            "\n",
            "4. Plant trees: Trees absorb carbon dioxide and release oxygen, making them essential for maintaining a healthy environment. Planting trees in urban areas or restoring degraded forests can help mitigate climate change.\n",
            "\n",
            "5. Support sustainable agriculture: Sustainable farming practices such as crop rotation, organic farming, and agroforestry can help reduce soil erosion, increase biodiversity, and promote healthier ecosystems.\n",
            "\n",
            "6. Advocate for policy changes: Governments have a critical role to play in promoting sustainability by implementing policies that encourage renewable energy, reduce pollution, and protect natural resources.\n",
            "\n",
            "7. Educate others: Raising awareness about environmental issues and encouraging others to take action can help create a culture of sustainability.\n",
            "\n",
            "By working together and adopting these strategies, we can make significant progress towards protecting our planet and building a sustainable future for generations to come.<|im_end|>\n",
            "Model: qwen/Qwen2.5-1.5B-Instruct have been released.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hIB9tc19igJe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}